
from IPython.display import Image
Image(r"C:\Users\acer\Desktop\data analysis process.png",width=300,height=200)


# for data Analysis import libreary
import pandas as pd
import numpy as np


# for Data visualization import libreary 
from matplotlib import pyplot as plt
import plotly.express as px


data=pd.read_csv("districts cases 2_4_2021.csv")
data.head()


data.shape

(36, 7)


data.isnull().sum()

Districts           0
positive_cases      0
active_cases        0
recoverd            1
deaths              0
recovery_rate(%)    1
deaths_rate(%)      0
dtype: int64


# find exact location of null value

null=data.columns[data.isnull().any()]
null

print(data[data['recoverd'].isnull()][null])

    recoverd  recovery_rate(%)
22       NaN               NaN


data.iloc[22]             # iloc-->access specific row

Districts           Other States/Country
positive_cases                       146
active_cases                          48
recoverd                             NaN
deaths                                96
recovery_rate(%)                     NaN
deaths_rate(%)                      65.8
Name: 22, dtype: object


remove_null=data.replace(np.nan,0).sum()


remove_null.isnull().sum()

0


# Drop  Other States/Country  row

# Drop using Dataframe index
clean_data=data.drop([22])
clean_data.head(25)


clean_data.duplicated().sum()

0


positive_total=clean_data.positive_cases.sum()
positive_total


active_total=clean_data.active_cases.sum()
active_total

display(positive_total,active_total)

2812834

356195


print("The number of Positive_cases in Maharashtra is {} And Number of Active_cases is {}".format(positive_total,active_total))

The number of Positive_cases in Maharashtra is 2812834 And Number of Active_cases is 356195


recoverd_cases=clean_data.recoverd.sum()


recovery_rate=  recoverd_cases / positive_total * 100
recovery_rate


display(recoverd_cases,recovery_rate)

2400727.0

85.34904654878318


print("The Recovery_Rate in Maharashtra is {:.2f}%".format(recovery_rate))

The Recovery_Rate in Maharashtra is 85.35%


death_cases=clean_data.deaths.sum()
death_cases


death_rate= death_cases / positive_total * 100
death_rate


display(death_cases,death_rate)

54553

1.9394319039090113


print("The Death_Rate in Maharashtra is {:.1f}%".format(death_rate))

The Death_Rate in Maharashtra is 1.9%


Image(r"C:\Users\acer\Desktop\cross check data.png",width=900,height=500)


fig_dims=(10,10)
fig,ax=plt.subplots(figsize=fig_dims)
name=['Districts']
clean_data.plot.bar(ax=ax)
plt.show()


clean_data.columns

Index(['Districts', 'positive_cases', 'active_cases', 'recoverd', 'deaths',
       'recovery_rate(%)', 'deaths_rate(%)'],
      dtype='object')


group_positive=clean_data.groupby('Districts')['positive_cases'].sum()
group_positive.head()

Districts
Ahmednagar    94044
Akola         28368
Amravati      49079
Aurangabad    83917
Beed          25985
Name: positive_cases, dtype: int64


px.bar(data_frame=group_positive,orientation='v',title='positive cases in Maharashtra')


top_deaths=clean_data[clean_data['deaths']>2000]
top_deaths


top_5=top_deaths.groupby('Districts')['deaths'].sum()
top_5

Districts
Mumbai    11690
Nagpur     3859
Nashik     2222
Pune       8325
Thane      5970
Name: deaths, dtype: int64


top_5.plot.pie(autopct='%0.1f%%',radius=2,subplots=True)
plt.show()


legend=['Mumbai','Nagpur','Nashik','Pune','Thane']
fig=px.pie(top_5,values='deaths',names=legend,title='Deaths cases in Maharasthra')
fig.show()


top_active=pd.pivot_table(clean_data,values='active_cases',index='Districts')
top_active.head()


px.line(data_frame=top_active,orientation='v',title='Line Graph of Active Cases in Maharashrta')


fig=px.scatter(clean_data,x='positive_cases',y='recoverd',color='Districts',orientation='v',
               title='positive cases over recoverd')
fig.show()


rate=clean_data[['Districts','deaths_rate(%)','recovery_rate(%)']]
rate


fig=px.scatter(rate,x='deaths_rate(%)',y='recovery_rate(%)',color='Districts',orientation='v',
               title='death_rate(%) And recovery_rate(%)')
fig.show()

	Districts	positive_cases	active_cases	recoverd	deaths	recovery_rate(%)	deaths_rate(%)
0	Ahmednagar	94044	9103	83728.0	1212	89.0	1.3
1	Akola	28368	4050	23850.0	464	84.1	1.6
2	Amravati	49079	2935	45494.0	648	92.7	1.3
3	Aurangabad	83917	19466	63095.0	1342	75.2	1.6
4	Beed	25985	4139	21223.0	614	81.7	2.4

	Districts	positive_cases	active_cases	recoverd	deaths	recovery_rate(%)	deaths_rate(%)
0	Ahmednagar	94044	9103	83728.0	1212	89.0	1.3
1	Akola	28368	4050	23850.0	464	84.1	1.6
2	Amravati	49079	2935	45494.0	648	92.7	1.3
3	Aurangabad	83917	19466	63095.0	1342	75.2	1.6
4	Beed	25985	4139	21223.0	614	81.7	2.4
5	Bhandara	17875	2477	15076.0	320	84.3	1.8
6	Buldhana	27768	3951	23527.0	285	84.7	1.0
7	Chandrapur	29016	2355	26215.0	444	90.3	1.5
8	Dhule	26031	5100	20562.0	365	79.0	1.4
9	Gadchiroli	10062	414	9530.0	110	94.7	1.1
10	Gondia	16026	869	14969.0	182	93.4	1.1
11	Hingoli	7007	1912	4995.0	100	71.3	1.4
12	Jalgaon	85443	6464	77358.0	1596	90.5	1.9
13	Jalna	23006	675	21920.0	410	95.3	1.8
14	Kolhapur	51301	731	48873.0	1694	95.3	3.3
15	Latur	33226	5545	26929.0	748	81.0	2.3
16	Mumbai	414773	49953	352173.0	11690	84.9	2.8
17	Nagpur	230187	46333	179950.0	3859	78.2	1.7
18	Nanded	43245	12268	30199.0	772	69.8	1.8
19	Nandurbar	18032	4525	13215.0	291	73.3	1.6
20	Nashik	178997	33442	143332.0	2222	80.1	1.2
21	Osmanabad	21395	2327	18464.0	587	86.3	2.7
23	Palghar	54072	3377	49711.0	974	91.9	1.8
24	Parbhani	14152	4560	9227.0	354	65.2	2.5
25	Pune	536262	64277	463611.0	8325	86.5	1.6

	Districts	positive_cases	active_cases	recoverd	deaths	recovery_rate(%)	deaths_rate(%)
16	Mumbai	414773	49953	352173.0	11690	84.9	2.8
17	Nagpur	230187	46333	179950.0	3859	78.2	1.7
20	Nashik	178997	33442	143332.0	2222	80.1	1.2
25	Pune	536262	64277	463611.0	8325	86.5	1.6
32	Thane	339590	39692	293897.0	5970	86.5	1.8

	Districts	deaths_rate(%)	recovery_rate(%)
0	Ahmednagar	1.3	89.0
1	Akola	1.6	84.1
2	Amravati	1.3	92.7
3	Aurangabad	1.6	75.2
4	Beed	2.4	81.7
5	Bhandara	1.8	84.3
6	Buldhana	1.0	84.7
7	Chandrapur	1.5	90.3
8	Dhule	1.4	79.0
9	Gadchiroli	1.1	94.7
10	Gondia	1.1	93.4
11	Hingoli	1.4	71.3
12	Jalgaon	1.9	90.5
13	Jalna	1.8	95.3
14	Kolhapur	3.3	95.3
15	Latur	2.3	81.0
16	Mumbai	2.8	84.9
17	Nagpur	1.7	78.2
18	Nanded	1.8	69.8
19	Nandurbar	1.6	73.3
20	Nashik	1.2	80.1
21	Osmanabad	2.7	86.3
23	Palghar	1.8	91.9
24	Parbhani	2.5	65.2
25	Pune	1.6	86.5
26	Raigad	2.0	92.4
27	Ratnagiri	3.3	92.3
28	Sangli	3.3	92.9
29	Satara	2.9	92.0
30	Sindhudurg	2.5	90.7
31	Solapur	2.8	88.9
32	Thane	1.8	86.5
33	Wardha	1.8	89.3
34	Washim	1.2	81.5
35	Yavatmal	1.9	83.3

Data Analysis And Visualization with Python¶

Data Analysis process¶

Load Data_CSV File¶

corona Cases 2/4/2021¶

Check Shape of data¶

Find null Values¶

Remove null values¶

Drop Unnecesary data¶

clean_data is new clean DataFrame¶

find duplicates¶

Analyzing Data¶

Total Positive And Active Corona Cases¶

Recovery Rate in Maharashtra¶

Death_Rate in Maharashtra¶

Cross Check Data¶

The Data Given below is dated 2/04/2021¶

Data Visualization¶

Bar Graph¶

Analyze data by Groupby values¶

positive_cases Bar Graph¶

Top 5 Districts with highest number of Deaths_cases¶

pie chart of Top 5 Districts with highest number of death_cases¶

Line Graph of Active Cases in Maharashrta¶

pivot table¶

Scatter plot of positive_cases over recoverd¶

scatter plot of deaths_rate(%) And recovery_rate(%)¶